1 citations · 2 across the 4 of their papers we have counts for
5 papers
Deep Operator Neural Network Model Predictive Control
Thomas Oliver de Jong, Khemraj Shukla, Mircea Lazar
In this paper, we consider the design of model predictive control (MPC) algorithms based on deep operator neural networks (DeepONets). These neural networks are capable of accurate…
Leveraging Deep Operator Networks (DeepONet) for Acoustic Full Waveform Inversion (FWI)
Kamaljyoti Nath, Khemraj Shukla, Victor C. Tsai +6
Full Waveform Inversion (FWI) is an important geophysical technique considered in subsurface property prediction. It solves the inverse problem of predicting high-resolution Earth…
A Neural Operator Emulator for Coastal and Riverine Shallow Water Dynamics
Peter Rivera-Casillas, Sourav Dutta, Shukai Cai +7
Coastal regions and river floodplains are particularly vulnerable to the impacts of extreme weather events. Accurate real-time forecasting of hydrodynamic processes in these areas…
Complex Valued Deep Operator Network (DeepONet) for Three Dimensional Maxwell's Equations:
Qile Jiang, Marc Salvadori, Dale Ota +2
Maxwell's equations, a system of linear partial differential equations (PDEs), describe the behavior of electric and magnetic fields in time and space and are essential for many im…
Randomized Forward Mode of Automatic Differentiation For Optimization Algorithms
Khemraj Shukla, Yeonjong Shin
We present a randomized forward mode gradient (RFG) as an alternative to backpropagation. RFG is a random estimator for the gradient that is constructed based on the directional de…